Mobile adaptive crowd optimization scheme based on distance field
 Yachun Tang^{1}Email author,
 Xiaobo Guo^{2} and
 Xiangdong Yin^{1}
DOI: 10.1186/s1363901600617
© The Author(s). 2016
Received: 4 August 2016
Accepted: 2 November 2016
Published: 15 November 2016
Abstract
For complexity and efficiency of the multiobjective optimization, we proposed the mobile distance fielddriven adaptive crowd optimization algorithm. In space, we modify the surface parameters based on the corresponding changes of the distance field to obtain the moving target’s moving track and moving surface. When the curve of the moving track is changed, the x axis and the y axis of the moving track are adjusted adaptively. In this paper, the moving process is divided into three processes: the target dynamic crowd control, the crowd model algorithm, and the predictive control of linear time domain based on the moving target prediction and crowd control algorithm. Then, the multiobjective optimization algorithm of moving objects is proposed by using the crowd model to predict the status and the position of the target. The experimental results show the high accuracy, low complexity, and high efficiency of the proposed optimization algorithm.
Keywords
Mobile crowd Adaptive optimization Distance field Multiple objects1 Introduction
Multiobjective optimization can be applied to a large number of practical applications [1]. The multiobjective optimization of the practical problems requires [2] simple architecture, easy implementation, low complexity, and low [3] and high optimization efficiency. However, the above features are often contradictory [4]. The search of a compromise between multiple characteristics [5] becomes the key to the development of multiobjective optimization applications.
The new subspace clustering algorithm was proposed by the authors of article [6], which segments the videos into consistent spatialtemporal regions with multiple classes. A novel algorithm was presented in article [7] that exploits joint optimization of representation and classification for robust tracking in which the goal is to minimize the leastsquares reconstruction errors and discriminative penalties with regularized constraints. In article [8], a multiobjective particle crowd optimization technique is applied to a group of consecutive frames to reduce the number of branches in each tracking tree. The novel algorithm was presented in the article [9] for the removal of reflections generated by objects on reflecting floors. The blockcoordinate GaussNewton/regression method was proposed by Kim D S et.al [10], in order to conduct a correlationbased registration considering the intensity difference between images in the presence of outlier objects. The projection image generation algorithm [11] was proposed to design and fabricate a complex 3D scaffold, which automatically and robustly generates 2D projection image data. The author of article [12] proposed a repeated use of screened Poisson to compute a part coding and extracting distance field. A highly scalable method was proposed in article [13] for computing 3D distance fields on massively parallel distributedmemory machines.
In view of the above achievements and problems, based on the multiobjective optimization model, we proposed adaptive mobile crowd optimization based on distance field. The rest of the paper is organized as follows: Section 2 describes the moving target distance field analysis model. Section 3 proposes the adaptive mobile crowd optimization. The performance evaluation is shown in Section 4. Finally, the conclusions are given in Section 5.
2 Moving target distance field analysis model
Here, S _{ C } represents the surface formed by the moving object. T _{ C } represents the moving trajectory of a moving target. The t represents the moving time of the target. T represents the total length of the moving target. Parameters x, y, and z represent the three component of the threedimensional space of the moving target. Parameter V _{0} represents the initial movement speed. Parameter a indicates the acceleration. Symbols m and n are the parameters of the moving surface. The moving surface is usually determined by the range of parameters. According to the formula (1), the parameters of the moving surface can be easily generated. In the form of moving surface parameters, the dot sequence of the moving surface is generated. We can get the moving surface by the linear processing of the store sequence and take the parameter value. Therefore, it is very convenient and efficient to form and update the moving surface. The time linearized by the moving surface has the characteristics of free rotation, fast state transition, and so on.
Here, ε is the threshold value of the curved surface. Parameter Consistent represents the degree of consistency between the moving objects in the threedimensional space and the curvature of the moving surface. Number 1 indicates complete agreement. Number 0 is not consistent, can be adjusted according to the distance field. Number 2 indicates that it is not consistent and cannot be adjusted.
Here, \( d\left(p,{S}_C\right)={\displaystyle \underset{p\in {S}_C}{ \lim }}\left({T}_C(t){S}_C(p)\right) \)
3 Adaptive mobile crowd optimization
3.1 Crowd model of moving target
Crowd model of the moving target is a linear fusion intelligent time domain mapping control model. The model is based on the cooperation of moving target prediction model and crowd control algorithm. The model includes the dynamic control, the crowd control model and the linear time domain predictive control. The crowd control can predict the status and position of moving objects by using crowd model. The crowd control model can design the mobile constraints and crowd optimization objectives based on the realtime mobile trajectory information. The model can generate the target state matrix and the target crowd information for the relationship mapping. The realtime continuous optimization is realized by crowd control with output information. The crowd algorithm can transform the tracking problem of moving objects to the target of the crowd optimization problem. This conversion is realized by using the crowd information of each moving target in the time domain to reset the optimization objective and the realtime optimal control conditions.
The controller of moving target crowd model is composed of crowd model and mapping model. The two submodels can be used to predict the status and position of moving targets in real time according to the moving trajectory and the moving surface. The model can provide the conditions for crowd optimization of moving objects by the objective state of different time domain.
According to the position of the moving target, the crowd distribution of time domain objects is predicted. The output state vector of the model is used to monitor the trajectory of moving objects and the moving surfaces. The objective optimization condition is provided according to the adjustment of crowd weight and change of linear time domain.
3.2 Adaptive multiobjective optimization

Step1. Select the appropriate sampling time. Determine the moving target. Speed and location of sampling are initialized randomly in an optimized range.

Step2. Get the surface by the moving objects in the moving process. Track of moving target is obtained. Tracing the time of moving targets. Get the total length of the moving time. Construction of threedimensional spatial component of moving objects. Get the initial movement speed. Obtain the acceleration of moving target according to the track and speed. Initialize of the parameters of the moving surface.

Step3. Select the state and position of the moving surface.

Step4. For each moment in the moving trajectory, the distance field function is obtained according to the formulas (2) and (3). According to this function, the velocity and position of the moving target are updated, and the new moving trajectory is obtained.

Step5. For each data point of the moving surface, the optimization goal would be set according to the method of distance field function shown in Fig. 1.

Step6. According to the method of Fig. 3, based on mobile positioning and crowd control, crowd optimization model is obtained for predicting and optimizing the moving goal, as well as the crowd acquisition.

Step7. The realtime decision of the upper control center is supported by mobile target trajectory control from mobile crowd multiple target optimization.

Step8. According to formula (6), the multiobjective optimization model of moving objects is obtained.
According to Fig. 4 and formula (5), step 3 realizes the adaptive multiobjective optimization.
4 Performance evaluation
Testing parameters
Parameter  Value  Parameter  Value 

T  10 m  x  200 m 
y  200 m  z  100 m 
a  5 m/s  V _{0}  2 m/s 
d  10 m  d _{TH}  20 m 
φ  0.3  ε  0.01 
5 Conclusions
We present the distance fielddriven adaptive mobile goals in intelligent optimization algorithms to solve the multiobjective optimization complexity and efficiency issues. First, based on the corresponding changes in the distance field, we modify the surface parameters in the airspace. In this way, the moving track and surface of the object can be obtained. Secondly, based on the curve structure of the moving track, we adaptively adjust the x axis and the y axis structure of the moving trajectory. Then, we decompose the process into three processes, which are target dynamic crowd control, crowd model algorithm control and linear time domain predictive control. The three processes are based on the cooperative control of the moving target prediction and crowd control algorithm to form a multiobjective optimization framework. Finally, the multiobjective optimization algorithm of moving objects is proposed by using the crowd model to predict the status and the position of the target. The results of the five test functions show that the proposed algorithm has higher localization accuracy, lesser iteration times, and smaller surface area than the target optimization algorithm based on mobile search.
Declarations
Acknowledgements
This work is supported in part by the Hunan Provincial Department of Education Scientific Research Outstanding Youth Project (14B070) and Science and Technology project of Hunan Province (2014FJ6095).
Competing interests
The authors declare that they have no competing interests.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Authors’ Affiliations
References
 G Zhu, Z Wang, G Zhang, Research on a combined device SMESSFCL based on multiobject optimization[J]. IEEE Trans. Appl. Supercond. 15(2), 2019–2022 (2005)View ArticleGoogle Scholar
 J Liu, L Cao, Z Li et al., Planebased optimization for 3D object reconstruction from single line drawings.[J]. IEEE Trans. Pattern Anal. Mach. Intell. 30(2), 315–327 (2008)View ArticleGoogle Scholar
 APD Poz, RAB Gallis, JFCD Silva, Threedimensional semiautomatic road extraction from a highresolution aerial image by dynamicprogramming optimization in the object space[J]. IEEE Geosci. Remote Sens. Lett. 7(4), 796–800 (2010)View ArticleGoogle Scholar
 P Fungtammasan, T Watanabe, Grasp input optimization taking contact position and object information uncertainties into consideration[J]. IEEE Trans. Robot. 28(5), 1170–1177 (2012)View ArticleGoogle Scholar
 J Stefanski, B Mack, B Waske, Optimization of objectbased image analysis with random forests for land cover mapping[J]. Sel. Top. Appl. Earth Obs. Remote Sens. IEEE J. 6(6), 2492–2504 (2013)View ArticleGoogle Scholar
 C Wang, Y Guo, J Zhu et al., Video object cosegmentation via subspace, clustering and quadratic pseudoBoolean optimization in an MRF framework[J]. IEEE Trans. Multimedia 16(4), 903–916 (2014)MathSciNetView ArticleGoogle Scholar
 Q Wang, F Chen, W Xu et al., Object tracking with joint optimization of representation and classification[J]. IEEE Trans. Circuits Syst. Video Technol. 25(4), 638–650 (2015)MathSciNetView ArticleGoogle Scholar
 K Ahmadi, E Salari, Small dim object tracking using a multi objective particle swarm optimisation technique[J]. IET Image Process. 9(9), 820–826 (2015)View ArticleGoogle Scholar
 D Conte, P Foggia, G Percannella et al., Removing object reflections in videos by global optimization[J]. IEEE Trans. Circuits Syst. Video Technol. 22(11), 1623–1633 (2012)View ArticleGoogle Scholar
 DS Kim, K Lee, Blockcoordinate Gauss–Newton optimization and constrained monotone regression for image registration in the presence of outlier objects[J]. IEEE Trans. Image Process. 17(5), 798–810 (2008)MathSciNetView ArticleGoogle Scholar
 DJ Yoo, Advanced projection image generation algorithm for fabrication of a tissue scaffold using volumetric distance field[J]. Int. J. Precis. Eng. Manuf. 15(10), 2117–2126 (2014)View ArticleGoogle Scholar
 M Genctav, A Genctav, S Tari, Nonlocal via local–nonlinear via linear: a new partcoding distance field via screened Poisson equation[J]. J. Math. Imaging Vision 55(2), 242–252 (2015)MathSciNetView ArticleMATHGoogle Scholar
 H Yu, J Xie, KL Ma et al., Scalable parallel distance field construction for largescale applications[J]. IEEE Trans. Vis. Comput. Graph. 21(10), 1187–200.s (2015)View ArticleGoogle Scholar